Bibliographic record
Abstract
Is the Canadian government contributing to its own underperformance? An insider’s guide to steering our nation toward greater efficiency Canada is struggling. Our growth is anemic, our standard of living is stagnant, our housing is unaffordable, and our health care system is nearing a breaking point. To make matters worse, the government fails to deliver core services and avoid management fiascos like the ArriveCan and Phoenix scandals. These failures are systemic and interconnected. What connects them is a fatal flaw in how the federal government operates, makes decisions, and takes action. Power has shifted from Cabinet to the Prime Minister and Prime Minister’s Office, with political staff taking on a larger role relative to the Public Service, and parliament has lost its ability to call the elected government to account. With 50 years of combined experience in leadership positions in government, authors Lynch and Mitchell offer an expert’s perspective on how to restore accountability and rebuild a culture of excellence by proposing a new blueprint for reshaping government. As Canadians face uncertainties at home and abroad, these practical and straightforward recommendations offer a path forward to ensure our nation’s prosperity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.018 | 0.031 |
| Scholarly communication | 0.022 | 0.010 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.021 | 0.006 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".